Systems for adjusting agronomic inputs using remote sensing, and related apparatus and methods

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media for using remote sensing data to infer agronomic inputs to an agronomic simulation model.

CROSS-REFERENCE TO RELATED APPLICATION(S)

This application claims priority to and the benefit of U.S. ProvisionalPatent Application Ser. No. 62/385,952, filed on Sep. 9, 2016, which ishereby incorporated by reference herein to the maximum extent permittedby applicable law.

TECHNICAL FIELD

This specification is directed towards systems and methods for agronomicsimulation based on analysis of remotely sensed agricultural data.

BACKGROUND

Agronomy is the science and technology of producing and/or using plants(e.g., for food, fuel, fiber, land reclamation, etc.). Agronomyencompasses work in the areas of plant genetics, plant physiology,meteorology, soil science, etc. An agronomic simulation model can beused to predict the agronomic output of a particular geographic regionbased on a set of agronomic inputs. The predictions output by anagronomic simulation model may be limited by the accuracy of the set ofinput data provided to the agronomic simulation model and the parametersused to configure the agronomic simulation model.

SUMMARY

The agronomic output (e.g., crop yield) of a geographic region (e.g.,field or farmable zone) may be influenced by the agronomic parameters(e.g., agricultural characteristics) of the field. Agriculturalcharacteristics may include, for example, biotic factors and non-bioticfactors. Agronomic simulators are sometimes used to predict theagronomic output of a geographic region based on the agronomic inputs tothe region. For example, agronomic simulators may be used to predict thecrop yield for a field, or to predict the effect of an agronomicintervention on the crop yield for a field.

In some instances, the agronomic inputs provided to an agronomicsimulator may omit or mischaracterize the values of one or moreagronomic parameters of a geographic region, and the failure to provideaccurate values for the agronomic parameters as inputs to the agronomicsimulator may have an adverse impact on the simulator's predictions ofthe geographic region's agronomic outputs (e.g., crop yield). Thus,techniques are needed for determining when the agronomic inputs to anagronomic simulator are incomplete or incorrect, and for inferring theexistence and/or values of agronomic inputs that can be used to improvethe predictions made by the agronomic simulator. Such techniques aredescribed herein.

In general, one innovative aspect of the subject matter described inthis specification can be embodied in a method including: identifying,based on data from an agronomic simulation model, a first indication ofexistence of a first agricultural characteristic in a particular portionof a first geographic region; receiving remote sensing data associatedwith the first geographic region, the received remote sensing datahaving been obtained using one or more remote sensing devices;identifying, based on the received remote sensing data, a secondindication of existence of a second agricultural characteristic in theparticular portion of the first geographic region; determining that thesecond indication is distinct from the first indication; and in responseto determining that the second indication is distinct from the firstindication, inferring one or more inputs to the agronomic simulationmodel based on the received remote sensing data to account for theexistence of the second agricultural characteristic as indicated by thesecond indication.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.A system of one or more computers can be configured to performparticular actions by virtue of having software, firmware, hardware, ora combination of them installed on the system that in operation causesor cause the system to perform the actions. One or more computerprograms can be configured to perform particular actions by virtue ofincluding instructions that, when executed by data processing apparatus,cause the apparatus to perform the actions.

The foregoing and other embodiments can each optionally include one ormore of the following features, alone or in combination. The actions ofthe method may include identifying, based on second data from anagronomic simulation model, a third indication of existence of a thirdagricultural characteristic in a particular portion of a secondgeographic region; receiving second remote sensing data associated withthe second geographic region, the received second remote sensing datahaving been obtained using one or more remote sensing devices;identifying, based on the received second remote sensing data, a fourthindication of existence of a fourth agricultural characteristic in theparticular portion of the second geographic region; determining that thefourth indication is substantially in accordance with the thirdindication; and based on determining that the fourth indication issubstantially in accordance with the third indication, confirming avalidity of the third indication of the existence of the thirdagricultural characteristic.

Determining that the fourth indication is substantially in accordancewith the third indication may include determining that the fourthindication substantially matches the third indication within a thresholdamount of consistency. The first agricultural characteristic may beindicative of pollination, evapotranspiration and/or tasseling. Theremote sensing data may include infrared measurements, thermalmeasurements, visible light measurements, near-infrared measurements,measurements of ultraviolet light and other forms of electromagneticradiation, and/or aerially collected remote sensing data.

Identifying, based on data from the agronomic simulation model, a firstindication of the existence of a first agricultural characteristic in aparticular portion of a first geographic region may include: providinggeographic data, other than the received remote sensing data, thatidentifies the particular portion of the first geographic region to anagronomic simulation model; and receiving an output from the agronomicsimulation model that includes data identifying one or more agriculturalcharacteristics that the agronomic simulation model predicts as existingwithin the particular portion of the first geographic region. The one ormore agricultural characteristics may be predicted by the agronomicsimulation model based on an evaluation of rainfall, soil hydraulicconductivity, and elevation.

Receiving remote sensing data associated with the first geographicregion may include receiving data indicative of one or more images ofthe first geographic region, the images having been captured by one ormore cameras. Identifying, based on the received remote sensing data, asecond indication of the existence of the second agriculturalcharacteristic in the particular portion of the first geographic regionmay include analyzing the one or more images of the first geographicregion to determine whether the one or more images include an indicationof the existence of the second agricultural characteristic. The secondagricultural characteristic may include ponding of water, tasselingand/or canopy growth. The one or more remote sensing devices may includea camera. Each of the one or more remote sensing devices may be coupledto a respective plane, drone, or satellite.

Inferring the one or more inputs may include adjusting one or moreparameters of an agronomic simulation model to account for the existenceof the second agricultural characteristic as indicated by the secondindication. Inferring the one or more inputs may further includeadjusting a set of agronomic inputs to the agronomic simulation model toaccount for the existence of the second agricultural characteristic asindicated by the second indication.

Determining that the second indication is distinct from the firstindication may include determining that the second indication based onthe remote sensing data identifies at least one agronomic characteristicthat is not modeled by the agronomic simulation model. The actions ofthe method may include updating the agronomic simulation model to modelthe identified at least one agronomic characteristic.

The first indication of the existence of a first agriculturalcharacteristic in a particular portion of a first geographic region mayinclude data indicating the non-existence of the first agriculturalcharacteristic. The second indication of the existence of a secondagricultural characteristic in the particular portion of the firstgeographic region may include data indicating the existence of the firstagricultural characteristic.

In general, another innovative aspect of the subject matter described inthis specification can be embodied in a method for using remote sensingdata to infer one or more inputs to an agronomic simulation model, themethod including: receiving remote sensing data associated with a firstgeographic region, the received remote sensing data having been obtainedusing one or more remote sensing devices; determining, based on thereceived remote sensing data, that one or more portions of the firstgeographic region are associated with a particular agriculturalcharacteristic; determining whether the particular agriculturalcharacteristic is produced by one or more biotic factors; and inresponse to determining that the particular agricultural characteristicis produced by the one or more biotic factors, inferring one or moreinputs to the agronomic simulation model to account for the one or morebiotic factors.

Other embodiments of this aspect include corresponding computer systems,apparatus, and computer programs recorded on one or more computerstorage devices, each configured to perform the actions of the methods.A system of one or more computers can be configured to performparticular actions by virtue of having software, firmware, hardware, ora combination of them installed on the system that in operation causesor cause the system to perform the actions. One or more computerprograms can be configured to perform particular actions by virtue ofincluding instructions that, when executed by data processing apparatus,cause the apparatus to perform the actions.

The foregoing and other embodiments can each optionally include one ormore of the following features, alone or in combination. The actions ofthe method may include: for the particular agricultural characteristic,calculating, by the agronomic simulation model, another agriculturalcharacteristic, with the particular agricultural characteristic beingattributable to the other agricultural characteristic. Calculating theother agricultural characteristic may include back-calculating the otheragricultural characteristic. The particular agricultural characteristicmay include an emergence date, and the other agricultural characteristicmay include a planting date. The particular agricultural characteristicmay be indicative of ponding, and the other agricultural characteristicmay be indicative of soil hydraulic conductivity.

The particular agricultural characteristic may be indicative ofpollination, tasseling, evapotranspiration, a canopy, and/or a plantstand count. Receiving remote sensing data associated with the firstgeographic region may include receiving data indicative of one or morecolor images of the first geographic region, the color images havingbeen captured by one or more cameras. Determining, based on the receivedremote sensing data, that one or more portions of the first geographicregion are associated with a particular agricultural characteristic mayinclude analyzing the one or more color images of the first geographicregion to determine whether the one or more color images indicate anexistence or value of a particular agricultural characteristic. Theparticular agricultural characteristic is an indication of agriculturalstress. Analyzing the one or more color images of the first geographicregion to determine whether the one or more color images indicate anexistence or value of a particular agricultural characteristic mayinclude analyzing the one or more color images of the first geographicregion to determine whether the one or more color images include one ormore indications of yellow vegetation. The one or more color images mayinclude one or more high-resolution color images.

Determining whether the particular agricultural characteristic isproduced by one or more biotic factors may include: providing geographicdata, other than the received remote sensing data, that identifies theone or more portions of the first geographic region associated with theparticular agricultural characteristic to an agronomic simulation model;and receiving an output from the agronomic simulation model thatincludes data indicating whether the particular agriculturalcharacteristic associated with each of the one or more respectiveportions of the first geographic region is caused by one or morenon-biotic factors. The actions of the method may include: based on adetermination that the output from the agronomic simulation modelindicates that the particular agricultural characteristic associatedwith each of the one or more respective portions of the first geographicregion is not caused by one or more non-biotic factors, determining thatthe particular agricultural characteristic is caused by one or morebiotic factors. The actions of the method may include: based on adetermination that the output from the agronomic simulation modelindicates that the particular agricultural characteristic associatedwith each of the one or more respective portions of the first geographicregion is caused by one or more non-biotic factors, determining that theparticular agricultural characteristic is not caused by one or morebiotic factors.

The one or more remote sensing devices may include a camera. Each of theone or more remote sensing devices may be coupled to a respective plane,drone, or satellite. The remote sensing data may include infrared remotesensing data, thermal measurements, and/or aerially collected remotesensing data. Inferring one or more inputs to the agronomic simulationmodel may include adjusting one or more parameters of the agronomicsimulation model to account for the one or more biotic factors. Thebiotic factors may include existence of fungi, insects, and/or weeds.The non-biotic factors may include soil pH, soil nitrogen levels, soilconsistency, soil depth, rainfall, phosphorus levels, and/or elevation.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. Agronomic inputs to an agronomic simulation modelcan be improved using remote sensing techniques. An agronomic simulationmodel can be trained over time based on an analysis of received remotesensing data. Customized intervention plans can be created to reduce(e.g., minimize) waste. Agronomic output (e.g., crop yield) can beincreased. Simulations can be performed using fewer computationalresources. The accuracy of agronomic outputs predicted by the agronomicsimulation model can be improved.

Details of one or more embodiments of the subject matter of thisdisclosure are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

The foregoing Summary, including the description of some embodiments,motivations therefor, and/or advantages thereof, is intended to assistthe reader in understanding the present disclosure, and does not in anyway limit the scope of any of the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Certain advantages of some embodiments may be understood by referring tothe following description taken in conjunction with the accompanyingdrawings. In the drawings, like reference characters generally refer tothe same parts throughout the different views. Also, the drawings arenot necessarily to scale, emphasis instead generally being placed uponillustrating principles of some embodiments of the invention.

FIG. 1A is a diagram of an example of a system for obtaining agronomicdata.

FIG. 1B is a contextual diagram of a system for using remote sensingdata to infer inputs to an agronomic model.

FIG. 2 is a block diagram of an example of a system for using remotesensing data to infer inputs to an agronomic model.

FIG. 3 is a flowchart of an example of a process for using remotesensing data to infer inputs to an agronomic model.

FIG. 4 is a flowchart of an example of a process for using remotesensing data to determine whether a detected agricultural characteristicof a geographic region is the result of biotic factors.

FIG. 5 is a diagram of a computer system.

DETAILED DESCRIPTION

The following terms may be used in the detailed description:

As used herein, a “remote sensing device” may refer to a device thatobtains information about a portion (or all) of the surface of the earth(or a geographic region) from a distance. Alternatively, or in addition,a “remote sensing device” may refer to a device that obtains informationabout a portion (or all) of the subsurface of the earth (or a geographicregion) from a distance.

As used herein, “remote sensing data” refers to data obtained by one ormore remote sensing devices.

As used herein, “agronomic parameters” may refer to one or moreagricultural characteristics and/or environmental characteristics (e.g.,of a geographic region, farmable zone, or candidate farmable zone).

As used herein, “agronomic simulation model” or “agronomic simulator”refers to a system that estimates and/or predicts an agronomic outputbased on one or more agronomic inputs.

As used herein, “agronomic input” or “input” refers to data (e.g., datacharacterizing agricultural characteristics, environmentalcharacteristics, etc.) that can be provided as input to the agronomicsimulation model. Agronomic inputs may characterize, for example,agronomic parameters.

As used herein, “agronomic output” or “output” refers to data that isoutput by an agronomic simulation model. Agronomic outputs maycharacterize the results of agronomic activity.

As used herein, a “geographic region” refers to a portion of the surfaceof the earth. Alternatively, or in addition, a “geographic region” mayrefer to a portion of the surface of any planet, asteroid, or othercelestial body. Alternatively, or in addition, a geographic region mayrefer to a portion of the surface of an indoor greenhouse.

As used herein, an “agricultural characteristic” refers to one or morecharacteristics related to the production and/or use of plants (e.g.,for food, feed, fiber, fuel, ornamentation, environmental or climaticmodification, etc.). Agricultural characteristics may include, forexample, cultivars and/or activities performed in the process offarming.

As used herein, a “set of agricultural characteristics” refers to agroup of one or more agricultural characteristics.

As used herein, “environmental characteristics” may refer to one or moreclimate conditions, weather conditions, atmospheric conditions, and/orsoil conditions (e.g., of a geographic region, farmable zone, orcandidate farmable zone). “Weather conditions” may include, but are notlimited to, precipitation (e.g., rainfall, snowfall, hail, or othertypes of precipitation), wind, and solar radiation. “Atmosphericconditions” may include, but are not limited to, carbon dioxide levels,ozone levels, and smog conditions. “Soil conditions” may include, butare not limited to, microbial presence, insect presence, weed presence,nematode presence, fungal organism presence, water table presence,location of water tables, and topography.

As used herein, “biotic factors” include “one or more living componentsthat have an influence on the agricultural characteristics of aparticular portion of, or all of, a geographic region.” Alternatively,or in addition, “biotic factors” may include “once-living componentsthat have an influence on the agricultural characteristics of aparticular portion of, or all of, a geographic region.”

As used herein, “non-biotic factors” include “one or more non-livingcomponents that have an influence on the agricultural characteristics ofa particular portion of, or all of, a geographic region.”

As used herein, “vegetation” refers to “one or more plants, algae, ormushrooms in a particular portion of a geographical region.”

As used herein, “ponding” refers to “a collection of water in aparticular portion of a geographical region.

As used herein, “canopy” refers to “a collection of the above groundportion of multiple plants formed by multiple plant crowns of aparticular geographical region.”

FIG. 1A is a diagram of an example of a system 100-A for obtainingagronomic data. The system 100-A may include at least one or morevehicles (e.g., a satellite 102-A, an airplane 104-A, or a tractor106-A), at least one agronomic data providing server 108-A, a server120-A, an agronomic database 140-A, and an agronomic data model 170-A.

Each of the vehicles may be equipped with one or more sensors capable ofcollecting agronomic data associated with a particular geographic region(e.g., a field of a farm). In some instances, the vehicles may include,for example, a satellite 102-A or an airplane 104-A equipped with one ormore remote sensing devices for capturing image(s) of at least a portionof a geographic location. The images may include, for example,red-blue-green images, thermal images, infrared images, radar images,etc. Alternatively, or in addition, the vehicles may include a tractor106-A equipped with one or more sensors capable of collecting agronomicdata related to a particular portion of a geographic location thatincludes, for example, a plant's location (e.g., GPS location), theplant's weight, the plant's time of harvest, etc. Other types ofvehicles may also be used to collect agronomic data associated with aparticular portion of a geographic location. Such vehicles may include,for example, a drone. The agronomic data 110-A, 111-A, 112A, 113-A,114-A, and 115-A captured by the vehicles may be transmitted via anetwork 130-A to a server 120-A. The network 130-A may include one ormultiple networks, for example, a LAN, a WAN, a cellular network, theInternet, etc.

Alternatively, or in addition, agronomic data 116-A and 117-A may beobtained from one or more agronomic data providing servers 108-A. Theserver 108-A may, for example, house a database of historic agronomicdata items from one or more geographic locations. For instance, theserver 108-A may provide access to a database (e.g., a database hostedby a government agency, university, etc.) that tracks changes inagronomic data associated with particular geographic locations overtime. The agronomic data 116-A, 117-A may be obtained from the server108-A via a network 130-A.

Server 120-A may process the data 110-A, 111-A, 112-A, 113-A, 114-A,115-A, 116-A, 117-A received via network 130-A and store 122-A thereceived data in an agronomic database 140-A. Processing the receiveddata 110-A-117-A by server 120-A may include extracting relevant aspectsof the received data for storage. Alternatively, or in addition,processing of the received data 110-A-117-A by server 120-A may includegenerating an index 150-A that can be used to efficiently access andretrieve the data 110-A-117-A once the data 110-A-117-A are stored asrecords 160-A in the agronomic database 140-A. The agronomic database140-A may be hosted on the server 120-A. Alternatively, or in addition,the agronomic database may be hosted by one or more other servers.

The index 150-A may include one or more fields for each index entry151-A, 152-A, 153-A, etc. Examples of index fields may include, forexample, a keyword field 150 a-A, a storage location field 150 b-A, etc.In the example of system 100-A, the agronomic database 140-A may beconfigured to receive one or more search parameters for one or moredatabase records (for example, search parameters requesting data relatedto “Field A”). In response to the receipt of such search parameters, theagronomic database 140-A may identify all the index entries matching thesearch parameter, identify the storage location 150 b-A associated witheach matching index entry, and access the database record(s) stored atthe identified storage location(s). Though a particular example of anindex 150-A and index fields 150 a-A, 150 b-A are provided herein, thepresent disclosure need not be so limited. Instead, any type of indexmay be used to index the data 110-A-117-A received and stored in theagronomic database 140-A so long as the data stored in the agronomicdatabase 140-A can be accessed by the agronomic data model 170-A.

The data 110-A-117-A may be stored in the agronomic database 140-A asone or more database records 160-A. The agronomic database 140-A maystore records in any logical database form (for example, a relationaldatabase, hierarchical database, column database, etc.). Instead ofrequiring the use of a particular logical database schema, the agronomicdatabase 140-A may only require a configuration that allows theagronomic data stored by the agronomic database 140-A to be accessed bythe agronomic data model 170-A. Some examples of the types of data thatmay be stored in agronomic database 140-A include a file 160 a-A (e.g.,an image file), a geographic location 160 b-A associated with the storedfile (or other agronomic data), a date 160 c-A the data were captured,or the like. Any suitable type of data may be stored, and in someembodiments the types of data stored are determined based on the type ofreceived data 110-A-117-A.

One or more server computers may provide access to the agronomic datamodel 170-A. The agronomic data model 170-A may request 172-A data fromthe agronomic database 140-A via a network 130-A. The requested data maybe data that can be used to analyze agronomic characteristics associatedwith a particular geographic location. Agronomic data responsive to theagronomic data model's 170-A request 172-A may be returned 174-A fromthe agronomic database 140-A to the agronomic data model 170-A via oneor more networks 130-A. The agronomic data model 170-A may use theagronomic data returned 174-A from the agronomic database 140-A as anagronomic input to the model.

FIG. 1B is a contextual diagram of a system 100-B for using remotesensing data to infer inputs to an agronomic model. In some embodiments,the system 100-B includes an agronomic input inference engine 110-B, anagronomic simulation model 120-B, multiple remote sensing devices 140a-B, 142 a-B, 144 a-B coupled to respective vehicles 140-B, 142-B,144-B, and a network 160-B.

The system 100-B can be used to increase an agronomic output (e.g., cropyield) of a farm 102-B by analyzing one or more agriculturalcharacteristics associated with each field of the farm 102-B. A field,e.g., field 105-B, is a geographic region associated with multipledifferent agricultural characteristics. The agronomic output (e.g., cropyield) of a particular field 105-B may be influenced by the agriculturalcharacteristics of the field. Such agricultural characteristics mayinclude, for example, rainfall, soil depth, soil pH, nitrogen levels,phosphorous levels, plant population, ponding, elevation, lateralrunoff, existence of irrigation pipes, etc. Agricultural characteristicsmay include, for example, biotic factors and non-biotic factors. Bioticfactors may include, for example, any living component that has aninfluence on the agricultural characteristics of a particular portion ofa geographic region. Non-biotic factors may include, for example, anynon-living component that has an influence on the agriculturalcharacteristics of a particular portion of a geographic region.

In some instances, agricultural characteristics may be influenced bynatural causes such as rainfall, elevation, lateral runoff, etc. Inother instances, agricultural characteristics may be influenced byactions resulting from human interaction with a field (e.g., nitrogenlevels, existence of irrigation pipes, etc.). Regardless of origin ortype, agricultural characteristics can be analyzed using system 100-B tooptimize crop yields for a particular field. In some instances, theexistence or values of one or more agronomic parameters of a geographicregion may be unknown, misinterpreted, etc., and the failure to provideaccurate values of these parameters as inputs to an agronomic simulationmodel may have an adverse impact on the model's predictions of thegeographic region's agronomic outputs (e.g., crop yield). Accordingly,the system 100-B facilitates a process for inferring the existenceand/or values of agronomic inputs that can be used to improve thepredictions made by the agronomic simulation model 120-B.

The agronomic input inference engine 110-B may interface with theagronomic simulation model 120-B. Interfacing with the agronomicsimulation model 120-B includes, for example, the agronomic inputinference engine 110-B providing 112-B an agronomic input to theagronomic simulation model 120-B and/or receiving 124-B an agronomicoutput from the agronomic simulation model 120-B. In someimplementations, the agronomic input inference engine 110-B and theagronomic simulation model 120-B may be made up of one or more softwareunits hosted, and executed by, the same computer, or group of computers.Alternatively, in other implementations, the agronomic input inferenceengine 110-B and the agronomic simulation model 120-B may be made ofseparate software units that are hosted by separate computers, orseparate groups of computers. In those implementations where theagronomic interference engine 110-B and the agronomic simulation model120-B are hosted by separate computers, or groups of computers,communications 112-B, 122-B, and 124-B may be facilitated through one ormore networks (not shown in FIG. 1B) (e.g., a LAN, a WAN, a cellularnetwork, the Internet, etc.).

The agronomic input provided 112-B to the agronomic simulation model120-B may include a set of agronomic inputs associated with the field105-B. The agronomic input 112-B may be obtained from a database ofagricultural characteristics maintained by the agronomic input inferenceengine 110-B. In one implementation, the database of agriculturalcharacteristics may include, for example, historical agricultural datathat is specific to the field 105-B, geographic region where field 105-Bis located, or both. For instance, the database of agriculturalcharacteristics may include, for example, values of agronomic parameters(e.g., rainfall, soil depth, soil pH, nitrogen levels, phosphorouslevels, plant population, ponding, elevation, etc.) that were measuredfrom field 105-B at specific points in time in the past. The obtainedagronomic input may then be provided 112-B to the agronomic simulationmodel 120-B to be processed. Some implementations of the agronomicsimulation model 120-B is discussed in further detail in U.S. patentapplication Ser. No. 15/259,030, titled “Agronomic Database and DataModel” and filed on Sep. 7, 2016, the contents of which are herebyincorporated by reference herein to maximum extent permitted byapplicable law.

The agronomic simulation model 120-B may predict a set of agronomicoutputs and provide 122-B the predicted set of agronomic outputs to theagronomic inference engine 110-B. The agronomic simulation model 120-Bmay predict a set of agronomic outputs by performing multiplecalculations in view of (e.g., based on and/or using) the receivedagronomic inputs. In some implementations, the agronomic simulationmodel 120-B may predict (e.g., calculate, or otherwise determine), forone or more agricultural characteristics in a set of agronomic inputs,whether the values of the first one or more agricultural characteristicsincluded within the set of agronomic inputs imply (e.g., result in) theexistence and/or value of a second agricultural characteristic includedwithin the set of agronomic outputs. For example, the agronomicsimulation model 120-B may predict (e.g., calculate) whether plants in aportion of a field will die given a predetermined amount of rainfall,soil composition, elevation, etc. In some implementations, the agronomicsimulation model 120-B may facilitate back-calculating. Back-calculatingmay include, for example, the agronomic simulation model 120-Bestimating the value of an agronomic output (e.g., the planting dates ofone or more plants) by accessing known data that is indicative of ahistorical emergence dates of one or more plants, accessing data that isindicative of historical weather patterns that are associated with ageographic region, and using the historical emergence dates andhistorical weather data to back-calculate the value of the agronomicoutput of interest (e.g., the planting date of one or more plants in aparticular geographic region). The emergence date may be, for example,the first date when a plant emerges from the ground after the seedassociated with the plant is planted.

Alternatively, or in addition, a predicted set of agronomic outputs mayinclude data indicative of the value, existence, non-existence, or levelof existence of one or more agricultural characteristics. The existence,or non-existence, of one or more agricultural characteristic may beindicated in any suitable way, for example, via a predicted crop yield,existence of a canopy, a plant stand count, etc. Alternatively, or inaddition, the existence, or non-existence, of one or more agriculturalcharacteristics may be indicated via a prediction indicating aparticular portion of a field is associated with a threshold amount ofagricultural stress. A particular portion of a field may be associatedwith more than a threshold amount of agricultural stress if theagronomic simulation model indicates that certain crops are likely to bekilled by non-biotic factors such as frost, anoxia, heat, drought, lackof sufficient ponding, excessive rainfall, excessive ponding, nitrogendeficiency, soil pH levels, soil consistency, phosphorus levels,elevation, etc. In some implementations, the predicted set of agronomicoutputs may be indicative of different levels of existence. Forinstance, a predicted set of agronomic outputs may be indicative of alevel of existence (e.g., light, moderate, heavy, etc.) of a particularagricultural characteristic (e.g., canopy, etc.). Similarly, thepredicted set of agronomic outputs may be indicative of, for example, ameasure of soil hydraulic conductivity, pollination, tasseling,evapotranspiration, etc.

A predicted set of agronomic outputs may represent or result in aconceptual snapshot (e.g., image) 130 a-B of the agriculturalcharacteristics associated with a field 105-B. The conceptual snapshot130 a-B may provide one or more indications of the value, existence,non-existence, or level of existence of one or more agriculturalcharacteristics 132 a-B, 134 a-B, 136 a-B. Each respective indication ofthe value, existence, non-existence, or level of existence of anagricultural characteristic may be associated with a location (e.g., aGPS location). The agronomic outputs may be provided in any suitableform (e.g., data in a text file, data in a database, data in aspreadsheet, or an image corresponding to a map of the field 105-B). Forexample, agronomic outputs may be provided in an image corresponding toa map of the field 105-B by coloring portions of an image of a map offield 105-B first and second colors (e.g., green or yellow) based onwhether the model predicts that plants at each respective GPS locationare predicted to live or predicted to die, respectively, given aparticular set of agronomic inputs processed by agronomic simulationmodel 120-B.

For instance, if the agronomic output indicates that a plant at aparticular GPS location is likely to survive, the corresponding portionof an image of a map of field 105-B may be colored green to indicatethat the plant at that location is predicted to survive given theparticular set of agronomic inputs processed by the agronomic simulationmodel 120-B. Alternatively, or in addition, if the agronomic outputindicates that a plant at a particular GPS location is predicted to die,the corresponding portion of an image of a map of field 105-B may becolored yellow to indicate that the plant at that location is predictedto die given the particular set of agronomic inputs processed by theagronomic simulation model 120-B.

In the example of FIG. 1B, the conceptual snapshot 130 a-B, generatedfrom a specific set of historical agronomic input data provided 112-B bythe agronomic input inference engine 110-B, indicates plants arepredicted to die at locations corresponding to indications 132 a-B, 134a-B, 136 a-B. In addition, the conceptual snapshot 130 a-B alsoindicates that the agronomic simulation model 120-B did not predict thatany plants would die in sector A of the field 105-B. The conceptualsnapshot 130 a-B is received 124-B by the agronomic input inferenceengine 110-B.

Though the example of FIG. 1B describes a scenario where the indications132 a-B, 134 a-B, 136 a-B are indicative of the locations and existenceof plants that are predicted to die, the present disclosure need not belimited to such indications. For instance, the indications 132 a-B, 134a-B, 136 a-B may be indicative of a certain level of ponding.Alternatively, the indications 132 a-B, 134 a-B, 136 a-B may beindicative of a certain level of canopy growth. Alternatively, theindications 132 a-B, 134 a-B, 136 a-B may be indicative of any agronomicoutput that can be predicted by the agronomic simulation model 120-B.

In some embodiments, the agronomic input inference engine 110-B caninterface with one or more remote sensing devices 140 a-B, 142 a-B, 144a-B, which may be coupled to respective vehicles 140-B, 142-B, 144-B, toobtain remote sensing data. For instance, a plane 140-B that is equippedwith a remote sensing device 140 a-B can fly over the field and use theremote sensing device 140 a-B to capture one or more images of one ormore portions 108 a-B of the field 105-B. The one or more capturedimages may be transmitted 150-B to the agronomic input inference engine110-B through the network 160-B using one or more wireless, or wired,communication links 170-B. The network 160-B may include one or more ofa LAN, a WAN, a cellular network, the Internet, etc. The images mayinclude any type of images including black-and-white images, colorimages on the red-blue-green spectrum, infrared images, near-infraredimages, thermal images, radar images, images representing ultravioletlight and/or other forms of electromagnetic radiation, etc.

Any suitable type of vehicle equipped with a remote sensing device maybe used to capture images of the field 105-B. For instance, a plane140-B can be used to capture images of one or more portions 108 a-B ofthe field 105-B (e.g., when a satellite is not available, or duringtimes of heavy cloud cover). Alternatively, or in addition, a satellite144-B may be used to capture images of one or more portions 108 c-B ofthe field 105-B (e.g., when the satellite is overhead, when it is toowindy for the plane 140-B to fly, or both. Alternatively, or inaddition, one or more drones (e.g., unmanned aerial vehicles or “UAVs”)142-B may be used to capture images of one or more portions 108 b-B ofthe field 105-B (e.g., when one or more portions of a field 105-B arenot within a line of sight of either the plane 140-B or satellite144-B). One or more drones 142-B may also prove useful, for example, incapturing images of a targeted portion 108 b-B of the field 105-B.Images captured by one or more drones 142-B and/or the satellite 144-Bmay be transmitted 152-B, 154-B through the network 160-B in the same,or substantially similar manner, as images transmitted 150-B from theplane 140-B.

One or more of the images transmitted 150-B, 152-B, 154-B by arespective vehicle 140-B, 142-B, 144-B equipped with a remote sensingdevice 140 a, 142 a, 144 a may be routed 180-B through the network 160.Referring to FIG. 1B, the image 130 b-B is representative of at leastone, or multiple, images captured by a remote sensing device 140 a-B,140 b-B, 140 c-B. The image 130 b-B may provide one or more indications132 b-B, 134 b-B, 136 b-B, 138 b-B of the existence, or non-existence,of one or more agricultural characteristics. The indications 132 b-B,134 b-B, 136 b-B, 138 b-B may include, for example, pixels representingthe existence of yellow plants indicating that the plants at thelocation of the field associated with the indications 132 b-B, 134 b-B,136 b-B, 138 b-B are dying. In the example of FIG. 1B, the image 130b-B, generated based on images captured from one or more remote sensingdevices 140 a-B, 142 a-B, 144 a-B and received 180-B through the network160-B, includes an indication 138 b-B of the existence of yellow plantsthat are dying in sector A of the field 105-B. The image 130 b-B isreceived 182-B by the agronomic input inference engine 110-B.

Though the example of FIG. 1B describes a scenario where the indications132 b-B, 134 b-B, 136 b-B, 138 b-B are indicative of the existence ofplants that are yellow and dying, the present disclosure is not limitedto such indications. For instance, one or more of indications 132 b-B,134 b-B, 136 b-B, 138 b-B may be indicative of a certain level ofponding. Alternatively, one or more of the indications 132 b-B, 134 b-B,136 b-B, 138 b-B may be indicative of a certain level of canopy growth.Alternatively, one or more of the indications 132 b-B, 134 b-B, 136 b-B,138 b-B may be indicative of any agronomic output that can be detectedbased on an output generated using a remote sensing device 140 a-B, 142a-B, 144 a-B.

The agronomic input inference engine 110-B may analyze the conceptualsnapshot 130 a-B that was generated based on the output of the agronomicsimulation model 120-B and the image 130 b-B that was generated based onthe output of one or more remote sensing devices 140 a-B, 142 a-B, 144a-B. The conceptual snapshot 130 a-B and the image 130 b-B may bereferred to as respective representations of the field 105-B. Analysisof the conceptual snapshot 130 a-B and the image 130 b-B may include,for example, comparing the conceptual snapshot 130 a-B to the image 130b-B to determine whether any differences exist with respect to theagricultural characteristics detected within each respectiverepresentation of field 105-B, to characterize (e.g., qualitativelycharacterize) such differences, and/or quantify such differences. Thecomparison may include, for example, an image analysis of the conceptualsnapshot 130 a-B, the image 130 b-B, or both to determine whether theconceptual snapshot 130 a-B and the image 130 b-B include the same, or adifferent, set of indications of agricultural characteristics. In oneimplementation, each indication of an agricultural characteristicidentified in the image 130 b-B may be compared to the indications ofagricultural characteristics identified in the conceptual snapshot 130a-B. Data indicating the existence of a particular agriculturalcharacteristic in the conceptual snapshot 130 a-B may be confirmed if,for example, the features associated with a particular geographiccharacteristic in the image 130 b-B match the features associated with aparticular geographic characteristic in the conceptual snapshot 130 a-Bwithin a predetermined similarity threshold. The features associatedwith a particular geographic characteristic may include, for example, aGPS location and one or more agricultural characteristics associatedwith the GPS location.

In some implementations, a combination of data processing and imageanalysis techniques may be used to facilitate the comparison between theconceptual snapshot 130 a-B and the image 130 b-B. For instance, in someimplementations, the conceptual snapshot 130 a-B may include aspreadsheet, text file, etc. that identifies a set of one or morecharacteristics associated with a particular location (e.g., GPSlocation). In such instances, image files received from one or moreremote sensing devices can be analyzed to detect the values, existence,or non-existence, of agricultural characteristics in a field 105-B, andthe results of the image analysis can be compared with data extractedfrom the conceptual snapshot (e.g., from the spreadsheet, text file,etc.) to determine whether the conceptual snapshot 130 a-B and the image130 b-B include the same, or a different, set of indications ofagricultural characteristics.

In the example of FIG. 1B, the agronomic input inference engine 110-Bcan detect a discrepancy between the respective representations of field105-B in Sector A. In particular, the agronomic input inference engine110-B determines that the agronomic simulation model 120-B failed topredict that plants would die in Sector A as indicated in conceptualsnapshot 130 a-B, whereas the indication 138 b-B in the image 130 b-Bgenerated based on the remotely sensed data indicates that plants diedin Sector A.

The determination, based in part on the remotely sensed data, that theagronomic simulation model 120-B failed to accurately predict thatplants in Sector A would die may indicate that the agronomic simulationmodel 120-B does not account for the agricultural characteristics thatare causing plants to die in Sector A. In one instance, the agronomicsimulation model 120-B may be trained to predict outputs using all knownnon-biotic factors. Thus, if it is determined, based in part on theremotely sensed data, that the death of certain plants in Sector A wasnot predicted by the agronomic simulation model 120-B, then it may beinferred that the death of the plants in Sector A was based on factorsnot accounted for by the agronomic simulation model 120-B (e.g., bioticfactors). Accordingly, the inputs to the agronomic model can be adjusted114-B to predict more accurate outcomes (e.g., crop yields) byaccounting for the presence of biotic factors causing the death ofplants in Sector A. For instance, inputs to the model can be provided toaccount for the presence of biotic factors including insects, weeds,fungi, etc.

Alternatively, or in addition, additional inputs can be provided to themodel to address the presence of the biotic factors (or otheragricultural characteristics detected based on remote sensing data).Such additional inputs may represent, for example, a predeterminedamount of weed killer, a predetermined amount of insecticide, increasedwater, etc. Then, the agronomic simulation model 120-B can process theadjusted/supplemented set of agronomic inputs to improve the accuracy ofthe agronomic outputs predicted by the agronomic simulation model 120-B.Moreover, the agronomic simulation model can be used to determine anapproach (e.g., intervention) to address the dead plants identified inSector A (or other portions of the field 105-B) by iteratively varyingthe inputs to the model (e.g., the values of the inputs) and analyzingthe outputs of the model to determine which inputs (e.g., input values)are predicted to yield improvement (e.g., greatest improvement) in theoutput of interest.

The number of times the simulation is iteratively run based on adjustedinputs may be significantly reduced as a result of the adjustedagronomic inputs inferred based on the remote sensing data. Thisreduction in simulation cycles reduces the amount of computationalresources used to improve agronomic outputs (e.g., crop yields) via theagronomic simulation model 120-B. Once the agronomic simulation model120-B has been configured to predict improved agronomic outputs (e.g.,crop yields), the agronomic inputs provided to the agronomic simulationmodel 120-B that produced the improved agronomic outputs can beimplemented in the real world on a corresponding field of a farm such asfield 105-B to yield the improved agronomic outputs in the real worldfield. Thus, the inputs to the agronomic simulator can be implemented asinputs on a real-world farm to improve real-world agronomic outputs(e.g., crop yield) of the farm.

By way of example, the agronomic inference engine 110-B may infer inputsto the agronomic simulation model 120-B based on received remote sensingdata. Then, the agronomic inference engine 110-B can adjust one or moreagronomic inputs to the model to address the agriculturalcharacteristics indicated by the received remote sensing data (forexample, in Sector A). Next, the simulation can be iteratively run(using the adjusted agronomic inputs as initial inputs, and varying theinputs across iterations of the simulation) until the outputs providedby the model 120-B (e.g., for the portion of the field in Sector A)match the agricultural characteristics indicated by the received remotesensing data.

In other implementations, the indication 138 b-B in Sector A mayindicate the existence of other types of agricultural characteristics offield 105-B. For instance, the indication 138 b-B of image 130 b-B mayindicate that there is ponding of water in Sector A, whereas theconceptual snapshot 130 a-B is not predicting ponding in Sector A. Basedon this determination, the inputs to the agronomic model can be adjusted114-B to predict more accurate outcomes (e.g., crop yields) byaccounting for the existence of ponding in Sector A.

Though benefits of the subject matter of the present disclosure arediscussed with reference to increasing crop yield, other benefits may beachieved by using the methods, systems, and computer-readable mediumdisclosed by this specification. For instance, the system 100-B may beused to decrease environmental impact of vegetation, decrease risk inplanting certain vegetation, etc. In some instances, benefits of thesubject matter disclosure by this specification may be merelyinformational and not result in a transformation of farm. For instance,in some implementations, the system 100-B may be used to determinewhether to underwrite an insurance policy. In other implementations, forexample, the system 100-B may be used to assist a person in determiningwhether to buy a farm or not. Other uses and benefits of system 100-Balso fall within the scope of the present disclosure.

Other implementations of the subject matter described in thisspecification may use remote sensing data to make specific calculationsin a direct manner. For instance, in one implementation a digitalelevation map of a geographic region may be obtained, weather historyfor the geographic region may be obtained, and one or more remotesensing images may be obtained. The system may use one or more overheadremote sensing images to determine the bounds of a pond. In someinstances, the system may use a DEM or a water shedding model to computea volume of water in the pond. In some instances, the system may obtainrainfall data and kSAT data. In some instances, the system may determinewhat value of kSAT is necessary given the agricultural simulation modeloutput provided with the known weather input to match the volume ofstanding water seen at in the remote sensing data at the time the remotesensing data was captured.

In some embodiments, determination of one or more agronomiccharacteristics may be made in an indirect manner. For instance, a patchof stand loss at a date/time T1 may be identified. In someimplementations, DEM or a water shedding model and/or recentprecipitation record (or other weather data) may be used to determinewhether the patch of stand loss at T1 is likely due to ponding (e.g.,because the patch is at a low point) and not some other characteristics(e.g., hail). In some implementations, a percentage of stand reductioncan be inferred. In some instances, the model has an anoxia routine. Inone example, assuming a complete stand or a particular value of standloss at an initial date/time T0, the precipitation record and thesaturated soil hydraulic conductivity (“kSAT”) can be provided as inputsto the model, and the kSAT can be iteratively varied to determine whatvalue of kSAT results in the observed amount of stand loss at T1.

FIG. 2 is a block diagram of a system 200 for using remote sensing datato infer inputs to an agronomic model. The system may include anagronomic input inference system 210, an agronomic simulation system220, one or more remote sensing devices 240-1, 240-2, 240-n, and anetwork 260.

The agronomic input inference system 210 may include one or morecomputers that each include at least a processing unit 211 and a memoryunit 213. The processing unit 211 includes one or more processorsconfigured to execute the instructions associated with each of one ormore software modules stored in the memory unit 213. The memory unit 213includes one or more storage devices (e.g., RAM, flash memory, storagedisks, etc.) The memory unit 213 stores software modules used to performthe actions of methods of the agronomic input inference engine describedby this specification. In particular, the software modules stored by thememory unit 213 may include a module implementing an agronomic inputinterference engine 215 that may be configured to perform the actions ofmethods as described with respect to FIGS. 1, 3, and 4. The agronomicinput inference engine 215 may include an agronomic simulation systeminterface unit 216, a remote sensing interface unit 217, and aninferencing unit 218. In addition, the agronomic input inference system210 includes an agronomic database 219. The agronomic database 219stores agronomic data (e.g., property specific agronomic inputs) basedon historical agricultural characteristics associated with one or morefields, one or more geographic regions, etc.

The agronomic simulation system interface unit 216 may facilitatenetworked communication between the agronomic input inference system 210and the agronomic simulation system 220. For example, the agronomicsimulation system interface unit 216 may function as a network interfacethat can transmit initial requests 212 to the agronomic simulationsystem 220. The initial requests 212 may include, for example, a set ofproperty specific agronomic inputs that were obtained from the agronomicdatabase 219. In some embodiments, the agronomic simulation systeminterface unit 216 is configured to receive communications from theagronomic simulation system 220 that include agronomic outputs 222. Theagronomic outputs 222 may include, for example, a set of agronomicpredictions related to a particular field, geographic region, etc. basedon the agronomic simulation system's 220 processing of a receivedagronomic input transmitted in the initial request 212. In someembodiments, the agronomic simulation system interface unit 216 isconfigured to provide a received output 222 from the agronomicsimulation system 210 to the agronomic input inference system's 210inferencing unit 218.

The remote sensing interface unit 217 may facilitate networkedcommunication between the agronomic input inference system 210 and oneor more remote sensing devices 240-1, 240-2, 240-n, where “n” is anypositive, non-zero integer. For example, the remote sensing unit 217 mayfunction as a network interface that can receive one or more images250-1, 250-2, 250-n transmitted by one or more respective remote sensingdevices 240-1, 240-2, 240-n. The images may be representative of theoutput of one or more sensors of respective remote sensing devices. Theimages may include any type of images including black-and-white images,color images on the red-blue-green spectrum, infrared images,near-infrared images, thermal images, radar images, images representingultraviolet light and/or other forms of electromagnetic radiation, etc.In some embodiments, the remote sensing interface unit 217 is configuredto provide one or more images 250-1, 250-2, 250-n to the agronomic inputinference system's 210 inferencing unit 218.

The inferencing unit 218 may analyze an agronomic output 222 from theagronomic simulation system 220 in view of one or more images 250-1,250-2, 250-n received from one or more remote sensing devices 240-1,240-2, 240-n, respectively. For example, the inferencing unit 218 maycompare agricultural characteristics identified in the agronomic output222 generated by agronomic simulation system's 220 processing of anagronomic input to agricultural characteristics indicated by one or moreimages 250-1, 250-2, 250-n obtained from respective remote sensingdevices 240-1, 240-2, 240-n. The comparison may result in thedetermination that (1) the agricultural characteristics predicted by theagronomic simulation system 220 and the agricultural characteristicsshown in remotely sensed images are the same (e.g., within a similaritythreshold), (2) the agronomic simulation system 220 predicted theexistence of agricultural characteristics that are not shown in aremotely sensed image, (3) the remotely sensed images show agriculturalcharacteristics that were not predicted by the agricultural simulationsystem 220, or (4) a combination thereof.

Based on the comparison, the inferencing unit 218 may generate andtransmit one or more adjusted inputs 214 to the agronomic simulationsystem 220. The adjusted inputs 214 can be provided to the agronomicsimulation system 220, for example, to train the agronomic simulationmodel 226 based on the agricultural characteristics identified in aremotely sensed image. Training the agronomic simulation model 226 mayinclude, for example, adjusting one or more parameters associated withan agronomic simulation model 226, altering one or more inputs providedto an agronomic simulation model 226, etc.

The agronomic simulation system 220 may include one or more computersthat each include at least a processing unit 225 and a memory unit 221.The processing unit 225 includes one or more processors configured toexecute the instructions associated with each of one or more softwaremodules stored in the memory unit 221. The memory unit 221 includes oneor more storage devices (e.g., RAM, flash memory, storage disks, etc.).The memory unit 221 stores software modules used to perform the actionsof methods of the agronomic simulation model 120-B described by thisspecification. In particular, the software modules stored by the memoryunit 221 may include modules implementing an agronomic simulation model226 and/or an agronomic input inferencing system interface unit 227. Insome implementations, the agronomic simulation system 220 may alsoinclude an agronomic database similar to agronomic database 219 storedby the agronomic input inference system 210. Alternatively, in oneimplementation, the agronomic simulation system 220 may host theagronomic database 219 and make the agronomic database 219 accessible tothe agronomic input inference system 210. The agronomic input inferencesystem interface unit 227 may facilitate networked communication betweenthe agronomic simulation system 220 and the agronomic input inferencesystem 210 via the network 260.

Though FIG. 2 depicts the agronomic simulation system 220 and theagronomic input inference system 210 as being separate components of thesystem 200, the present disclosure need not be so limited. For instance,in one implementation, the agronomic simulation system 220 and theagronomic input inference system 210 may be hosted by the same computer,or same group of computers.

The system 200 may include one, or multiple, remote sensing devices240-1, 240-2, 240-n, where “n” is equal to any positive, non-zerointeger value. The remote sensing device 240-1 includes a processingunit 241-1, a memory unit 242-1, a network interface 243-1, and one ormore remote sensing sensors 244-1. The processing unit 241-1 includesone or more processors configured to execute the instructions associatedwith each of one or more software modules stored in the memory unit242-1. The memory unit 242-1 includes one or more storage devices (e.g.,RAM, flash memory, storage disks, etc.). The memory unit 242-1 storessoftware modules used to operate the remote sensing device 240-1including, for example, the operation of one or more remote sensingsensors 244-1. Operation of the remote sensing sensors 244-1 includes,for example, powering on the remote sensing sensor 244-1, aiming theremote sensing sensor 244-1, focusing the remote sensing sensor 244-1,capturing one or more images using a remote sensing sensor 244-1,transmission of captured images using the network interface 243-1, etc.The remote sensing sensors 244-1 may include one or more of a digitalcamera, a thermal imaging device, an infrared imaging device, radar,ultraviolet imaging device, ground penetrating radar, representativesensors embedded into living plants, plants on the surface, rain gauges,soil probes, etc. In some implementations, the remote sensing sensorsmay include, for example, active sensing sensors (e.g., LIDAR or RADAR).The remote sensing device 240-1 may be configured to transmit one ormore capture images 250-1 to the agronomic input inference system 210via a network 260. Alternatively, captured images may be manuallytransferred from the remote sensing device 240-1 to the agronomic inputsystem 210 using a removable storage device (e.g., a Universal SerialBus (USB) storage device). Each of the one or more remote sensingdevices 240-1, 240-2, 240-n can be mounted to a vehicle, for example, aplane, a drone, land-based rover, a satellite, a combine, etc.

Though the remote sensing devices described herein may include computerdevices including a processor, memory, etc., a remote sensing deviceneed not be so limited. For instance, a remote sensing device mayinclude a chemical sensor that accumulates intercepted hydrogen ions andthen turns a color. In such instances, the chemical sensor may itself beconsidered a remote sensing device that may be read by a remote camera.In a similar manner, other remote sensing devices may include a stickybright paper and a pheromone to attract and count insects. Accordingly,remote sensing devices that do not include a processor, memory, or otherelectrical components also fall within the scope of the presentdisclosure.

FIG. 3 is a flowchart of a process 300 for using remote sensing data toinfer inputs to an agronomic model. For convenience, the process 300 isdescribed as being performed by a system of one or more computerslocated in one or more locations. For example, a system 200 forinferring inputs to an agronomic simulation model 226, appropriatelyprogrammed in accordance with this specification, can perform theprocess 300.

The process 300 begins with the system identifying 310, based on datafrom an agronomic simulation model, a first indication of the value orexistence of a first agricultural characteristic. The system mayidentify the first indication of the value or existence of a firstagricultural characteristic by analyzing an agronomic output provided byan agronomic simulation model to detect the value or presence (or lackthereof) of one or more particular agricultural characteristics. Thefirst indication of the value or existence of a first agriculturalcharacteristic may include data that indicates that a particularagricultural characteristic exists or has a particular value at aparticular location of a field or data that indicates that a particularagricultural characteristic does not exist at a particular location of afield. In one implementation, a first indication of the value orexistence of a first agricultural characteristics may include, forexample, the existence or non-existence of the ponding of water, deadplants, yellow plants, a canopy, pollination, evapotranspiration,tasseling, etc. Data indicating the value, existence, or non-existence,of a particular agricultural characteristic may include, for example, aGPS location and a vector that is representative of the one or moreagricultural characteristics associated with the GPS location.

The system receives 320 remote sensing data from one or more remotesensing devices. The remote sensing data may include, for example, oneor more images of a field captured by a remote sensing device mounted toa vehicle. The images may include any type of images includingblack-and-white images, color images on the red-blue-green spectrum,infrared images, near-infrared images, thermal images, radar images,images representing ultraviolet light and/or other forms ofelectromagnetic radiation, etc. The system can analyze the remotesensing data to identify 330 a second indication of the value orexistence of a second agricultural characteristic. The second indicationof the value or existence of a second agricultural characteristic mayinclude data that indicates that a particular agriculturalcharacteristic exists or has a particular value at a particular locationof a field or data that indicates that a particular agriculturalcharacteristic does not exist at a particular location of a field. Inone implementation, a second indication of the existence of a secondagricultural characteristic may include, for example, a determinationthat an image from a remote sensing device depicts ponding of water,dead plants, yellow plants, a canopy, pollination, evapotranspiration,tasseling, etc.

The system determines 340 whether the second indication is differentthan the first indication. Such a determination may include, forexample, a comparative image analysis between the output of theagronomic simulator that yielded the first indication and one or moreimages obtained from one or more remote sensing devices. Thisdetermination allows the system 340 to confirm whether the actual imageof the field captured by a remote sensing device confirms the existence,or non-existence, of the agricultural characteristics suggested by thefirst indication.

In response to determining that the second indication is different thanthe first indication, the system infers 350 a revised set of agronomicinputs to the agronomic simulation model based on the received remotesensing data to account for the value, existence, or non-existence, ofthe second agricultural characteristic as indicated by the secondindication. For example, the inputs can be adjusted to indicate thatremote sensing data shows that a particular portion of a field includesponding, a canopy, tasseling, etc. Alternatively, the system maydetermine that the second indication is not different than the firstindication. In response to determining that the second indication is notdifferent than the first indication, the system may provide an input tothe agronomic simulation model that reinforces the agronomic simulationmodel for making an accurate prediction.

FIG. 4 is a flowchart of a process 400 for using remote sensing data todetermine whether a detected agricultural characteristic of a geographicregion is the result of (e.g., is explained by or depends on) bioticfactors. For purposes of convenience (and without limitation), theprocess 400 is described as being performed by a system of one or morecomputers located in one or more locations. For example, a system 200for inferring inputs to an agronomic simulation model 226, appropriatelyprogrammed in accordance with this specification, can perform theprocess 400.

The process 400 begins with the system receiving 410 remote sensing dataassociated with a first geographic region. The remote sensing data mayinclude, for example, one or more images of a field captured by a remotesensing device mounted to a vehicle. The images may include any type ofimages including black-and-white images, color images on thered-blue-green spectrum, infrared images, near-infrared images, thermalimages, radar images, images representing ultraviolet light and/or otherforms of electromagnetic radiation, etc. The first geographic region maycorrespond to at least a portion of a field that is associated with afarm.

The system analyzes the remote sensing data received at stage 410 todetermine 420 whether the remote sensing data indicates that one or moreportions of the first geographic region are associated with a particularagricultural characteristic. The remote sensing data may include, forexample, an indication that at least a portion of the first geographicregion is under agricultural stress. An indication of agriculturalstress may include, for example, the identification of one or morepatches of yellow vegetation. In response to determining that the remotesensing data includes an indication that at least a portion of the firstgeographic region is associated with the particular agriculturalcharacteristic, the system may determine 430 whether the particularagricultural characteristic is produced by one or more biotic factors.Biotic factors may include any living component having an influence onan agricultural characteristic of the first geographic location, forexample, weeds, fungi, insects, etc.

Determining 430 whether the particular agricultural characteristicdetected in the remote sensing data is produced by one or more bioticfactors may include, for example, determining whether the particularagricultural characteristic detected in the remote sensing data isproduced by one or more non-biotic factors. Non-biotic factors mayinclude any non-living component that has an influence on anagricultural characteristic of the first geographic location, forexample, soil nitrogen levels, soil pH, elevation, soil phosphorouslevels, amount of sunlight, amount of rainfall, drought, or the like.

Determining 430 whether the particular agricultural characteristic isproduced by one or more biotic factors may include, for example,interaction with an agronomic simulation model. For instance, the systemmay provide an agronomic input to an agronomic simulation model andrequest a set of agronomic output data predicted by the agronomicsimulation model based on the agronomic input. In some implementations,the agronomic input may include, for example, data other than the remotesensing data received from the remote sensing devices. For instance, theagronomic input may include, for example, geographic data thatidentifies the one or more portions of the first geographic region thatare associated with the particular agricultural characteristicidentified at stage 420.

Determining 430 whether the particular agricultural characteristic isproduced by one or more biotic factors may include receiving anagronomic output from the agronomic simulation model that is based onprocessing the agronomic input that includes the geographic data. Thereceived agronomic output may include a predicted crop yield.Alternatively, or in addition, the agronomic output can indicate whetherany of the multiple non-biotic factors accounted for by the agronomicsimulation model predict the death of plants at the geographic locationidentified in the agronomic input. If the agronomic output indicatesthat plants at the geographic location identified in the agronomic inputwere predicted to die, then it can be concluded that the particularagricultural characteristic identified in the remotely sensed data iscaused by non-biotic factors. If, on the other hand, the agronomicoutput indicates that plants at the geographic location identified inthe agronomic input were predicted to live, then it can be concludedthat the particular agricultural characteristic identified in theremotely sensed data is produced by biotic factors that are notaccounted for by the agronomic simulation model.

In response to determining 440 that the particular agriculturalcharacteristic identified in the remotely sensed data is produced by oneor more biotic factors, the system may infer one or more inputs to theagronomic simulation model to account for the biotic factors. Inferringone or more inputs to account for the biotic factors may include, forexample, adjusting the value associated with one or more inputparameters of an agronomic simulation model to account for the existenceof the biotic factors. For example, an agronomic input may be generatedthat includes the addition of a predetermined amount of fertilizer,insecticide, etc. that can be used to treat the biotic factors producingthe particular geographical characteristic identified in the remotelysensed data.

In some implementations, the subject matter of the present disclosuremay be used to generate a stability map. Generating a stability map mayinclude, for example, analyzing the variance in the values correspondingto one or more agronomic characteristics across multiple neighborhoodsN0 . . . Nm that each share at least one common boundary with another ofthe neighborhoods N0 . . . Nm. An “unstable region” may be a homogeneousregion that may obscure some underlying heterogeneity. For instance, byway of example, two neighboring patches could both have high variancethrough time and thus both be marked “unstable” even though Patch A ishigh yielding in high precipitation years and low yielding in lowprecipitation years, while Patch B is high yielding in low precipitationyears and low yielding in high precipitation years.

In some implementations, stability maps (e.g., zones) may be generatedbased on data obtained from one or more remote sensing devices. Suchstability zones may be determined by, for example, determining the meanand the standard deviation through time of a series of inputs.

The inputs may include yield maps. Alternatively, or in addition, theinputs may be images from one or more remote sensing devices.Alternatively, or in addition, the system may mean-center each image tonormalize it. Alternatively, or in addition, the system may determinethe standard deviation of the pixels (e.g., normalized pixels) throughtime. In some implementations, ranges of the mean and ranges of thestandard deviation may define the zones. For example, zones withstandard deviation ≧a threshold value (e.g., 15) may be classified as“unstable,” zones with standard deviation <the threshold value may beclassified as “stable.” Zones with means less than a first threshold(e.g., −10) may be classified as “low,” zones with means greater than asecond threshold (e.g., 10) may be classified as “high,” and zones withmeans between the first and second thresholds may be classified as“medium.” Using this classification scheme, six types of zones may beidentified (e.g., unstable/low, unstable/medium, unstable/high,stable/low, stable/medium, and stable/high).

In some implementations, the remote sensing data may include, forexample, normalized difference vegetation index (NDVI). NDVI may becollected multiple times over a predetermined time period (e.g., amonth, multiple months, a year, etc.). One or more of these images atdifferent times may be used to compute the maps. One or more of theimages may be excluded from the set used to compute the maps. Forinstance, an image may be excluded if the image includes, for example,clouds. One or more functions may select which image(s) to use.

Such functions may, for example, perform one or more operations such asimproving (e.g., maximizing) the amount of contrast of the image of thefield. In some implementations, the functions may perform suchoperations only for pixels that represent plants. Alternatively, thefunction may instead be based on the growth stage of the plant. Themodel may be executed for the time period (e.g., year) on a periodic(e.g., daily) timestep to predict the plant growth stage for each period(e.g., day), and the predictions (e.g., predicted developmental stage)for a particular period (e.g., day) may be compared to the correspondingimage(s) for that period (e.g., day), if any. An image may not exist forevery period (e.g., day). In some implementations, the function forchoosing the image to use may be based on trying to find the imageclosest to a given developmental stage of the crop, for instance V8 forcorn, V6 for corn, or the like.

Alternatively, or in addition, elevation data may be used to analyze theagronomic characteristics of a particular geographic region. Elevationdata may be analyzed using, for example, a high resolution DigitalElevation Model. The high resolution Digital Elevation Model may includea layer of surface height at every pixel. Alternatively, or in addition,the Digital Elevation Model may be based on irregular pixels orpolygons. Alternatively, or in addition, other resolutions can be used.

Alternatively, or in addition, a water shedding model may be used toanalyze agronomic characteristics of a particular geographic region. Awater shedding model may include, for example, D-Infinity. In someimplementations, the water shedding model may consider soilinfiltration, evaporation, plant water use, etc. as the water moves tocalculate the water flow along a field. Alternatively, or in addition,the water shedding model may account for lateral flow to calculate thewater flow along a field. Alternatively, or in addition, the watershedding model may consider the effects of farm field drainage lines,waterways or streams in the model. Alternatively, or in addition, thewater shedding model may consider areas outside the geographic region ofinterest draining to or from the geographic region under consideration.Alternatively, or in addition, the water shedding model may consider thedifferent infiltration rates of water into the soil (and thus, e.g.,decreasing the amount of runoff) of different soil zones across thegeographic region (e.g., a sandier zone through which water filtersfaster, thus contributing less water flow runoff to other zones).Alternatively, or in addition, the water shedding model may consider thecurrent saturation level of the soil in each of these zones to alter theamount of runoff (e.g., if a region is already saturated, more waterruns off, and this is a quantitative effect over different levels ofsoil saturation). This may be computed from a soil hydrology model andthe actual or simulated weather to date. Alternatively, or in addition,in determining the amount of runoff, the water shedding model mayconsider plant water uptake and evaporation. Alternatively, or inaddition, the water shedding model may consider capillary action of soildrawing water up from below. The water shedding model may account forthe “intensity” of the rain. The “intensity” of the rain may include,for example, the spacing of the rainfall in time. For example, if 2 cmof rain falls over only 2 minutes instead of over 2 hours, more runoffmay occur as there is not enough time for as much water to intercalateinto the soil. This may be, for example, actual spacing in time such asfrom hourly data or higher resolution in time RADAR or other measurementmodalities, or it may be a proxy variable. For example, in the U.S.,rainfall is generally more intense in the summer than in the winter.

In some implementations, the soil hydraulic conductivity (e.g., the ratethat water can move through the soil) of each patch of soil can be usedto calculate some amount of incident water absorbed and some amount ofrunoff. Alternatively, or in addition, the model could model some watercoming up from capillary action, some water drawn up by plants, somewater lost due to evaporation etc. During a precipitation event, eachsoil patch may be both receiving incoming precipitation and possiblycontributing runoff to some of its neighbors.

In some implementations, a total amount of runoff may be transitivelycalculated for each pixel. Some amount of the incident rain may beabsorbed by soil patch A, some amount may run off to soil patch B, andin some instances, another amount may run off into one or more othersoil patches. Soil patch B may also have its own same incident rain, towhich the runoff from Soil Patch A, and possibly other tiles, may beadded. Some amount of this total may be absorbed into the soil at patchB, and some amount may run off to soil Patch C (and possibly others).

In some implementations, an “incident water mask” that accounts for theflow induced by topography can be calculated. The incident water maskmay include a raster (e.g., a set of pixels that cover a geographicregion (e.g., a farm field)) or vector (e.g., set of polygons that coverthe geographic region (e.g., a farm field)) of weights, such that theExpectation (e.g., probability theory expectation such as thearea-weighted mean) is 1. For example, if there exists R centimeters ofrain on a geographic region (e.g., field), instead of assigning everyzone of the geographic region (e.g., field) where the model is run toexperience R cm of rain, the model can multiply that R cm by thecorresponding water mask polygon's weight and use the resulting value asthe rain for the model. For example, a geographic region (e.g., field)may have a depression in the center. Because the soil has some moistureand cannot absorb all of the incident rain at every point in space, someof the rain may run off of the soil surface and into the depression.This may result in the upper zone of the field experiencing only X<R cmof rain, and the depression experiencing Y>R cm of rain.

A static mask may be created to use for running the model. This staticmask may be computed every hour and be used to simulate the entire soilhydrology system to supply soil moisture to the model.

Alternatively, or in addition, a much smaller series of masks may becomputed in an effort to improve the computational efficiency of theabove hourly process. For example, a mask could be computed for eachmonth of the year. The mask, in at least one example, may correspond toone or more determinations of rainfall intensity. Alternatively, or inaddition, the mask could be computed for each of the product of a seriesof aggregate initial soil moisture levels times the series of months ofthe year, thus taking into account 2 variables—e.g., the existing soilmoisture (e.g., more initial moisture yields more runoff) and therainfall intensity (e.g., greater intensity of rainfall yields morerunoff). Alternatively, or in addition, the system can compute a singlemask with an aggregate value of initial soil moisture (e.g., averagesoil moisture during the growing season) and an aggregate value ofrainfall intensity (e.g., average value of rainfall intensity during thegrowing season). In any of these cases, the soil water content for eachzone can be initialized, and then a rainfall event can be run insimulation using the specified rainfall intensity. The time to moverunoff water between soil patches may occur in either continuous ordiscrete time. It may be assumed that this water transfer time is zero.Assuming the transfer time is zero can improve the computationalefficiency of the process versus assuming steps in time.

In some implementations, all of the zones may be represented using atransition matrix. For example, each cell of the matrix may represent anamount of flow of water from a soil patch indicated by the row label ofthe matrix to the soil patch indicated by the column label of thematrix. When the row and column labels of a matrix cell are the same,that cell indicates the amount of water that will remain on theassociated soil patch, and/or be absorbed into the soil. Suchimplementations may not be sensitive to the choice of flow directionfrom row patches to column patches. Alternatively, a flow may beexpressed as from column patches to row patches. Alternatively, the flowmay be represented as a weighted graph of soil patch nodes and edgeswith the transfer weight. In such an implementation, each row isnormalized such that the values in the row sum to 1, meaning that thepercentage of the water that flows to any of the other nodes or stays atthe current node sums to 1, meaning that water is conserved (e.g., notcreated or destroyed). In this way the flow matrix may be a probabilitymatrix. The matrix may be construed to represent a Markov chain, and thelong term equilibrium state may be computed by, for example, beingapproximated by matrix exponentiation, solved using the inverse of thetransition matrix, etc.

The generated mask (or masks), as it takes into account the differentsoil polygons/rasters for each farm field and thus the different soilhydrological processes (e.g., different intercalation rates), may bedifferent for every field.

In some implementations, the system may construct a field specific watermask, collect precipitation data (and potentially other data) to run acrop model, multiply the mask by the precipitation data to create amodified per-zone incident rain value, use the modified value as inputto the crop model, and predict one or more agronomic outputs based onthe inputs to the crop model.

Further Description of Some Embodiments

Agronomic inputs can include both a type of agronomic input (e.g.,sandiness) and a value for the agronomic input (e.g., 20%). In general,a change in an agronomic input refers to a change in the value for theagronomic input. Examples of agronomic inputs can include, but are notlimited to: maximum ponding height; soil layer depth; saturated soilwater content; soil bulk density; soil organic carbon content; soil claycontent; soil sand content; soil silt content; soil stones (coarsefragment) content; lower limit of soil water availability; drained upperlimit of soil water availability; saturated soil hydraulic conductivity;soil nitrogen content; soil pH; soil cation exchange capacity; soilcalcium carbonate content; soil fresh organic matter (FOM) carbon,nitrogen and phosphorus content; soil active inorganic carbon content;soil slow inorganic carbon content; soil active inorganic phosphoruscontent; soil slow inorganic phosphorus content; soil mineral nitrogenincluding nitrate, ammonia and urea; air temperatures (including minimumand/or maximum); soil temperatures (including minimum and/or maximum);storm intensity (tightness of precipitation in time, for example, 1″over 5 hours or in 5 minutes); elevation; solar radiation;precipitation; relative humidity; planting date; planting window dates;temperate thresholds for planting; soil moisture thresholds forplanting; crop row spacing; planting depth; crop species; cropvariety/cultivar; yield components of the variety/cultivar (for example,beans per pod, pods per plant, kernels per ear, ears per plant, etc.);length of developmental stages of variety/cultivar; compression ofdevelopmental stages of variety/cultivar; planting density; fieldirrigation; irrigation event water volume; irrigation event dates;irrigation drain depth; irrigation drain spacing; fertilizer date;fertilizer amount; fertilizer type (for example, manure, anhydrousammonia, etc.); chemical composition of fertilizer type; fertilizerapplication depth; fertilizer incorporation percentage; harvest date;percent of stalk/leaves knocked down at harvest; percent of plantby-product harvested (leaves, etc.); percent of grain/fiber/fruit/etc.harvested; insect activity; plant hypoxia; weed growth; disease.

Agronomic outputs can include both a type of agronomic output (e.g.,crop yield) and a value for the agronomic output (e.g., 175bushels/acre). In general, a change in an agronomic output refers to achange in the value for the agronomic output. Examples of agronomicoutputs may include, but are not limited to, crop yield; sustainability;environmental impact; length of developmental stages ofvariety/cultivar; yield; leaf area index (LAI) over time; damage/deathto the crop by frost, anoxia, heat, drought, etc.; dry weight ofgrains/fiber/fruit/veg; dry weight of shoots/areal plant parts; rootdepth; total root dry weight; change in biomass from previous timeslice; daily and accumulated thermal time; radiation use efficiency;relative thermal time to maturity; current plant development phase; rootweight, and of tillers; grain weight, and of tillers; total accumulatedleaves or their equivalents; total accumulated phylochron intervals;leaf weight, and of tillers; weight of stem reserves, and of tillers;weight of stems, and of tillers; sink weight; source weight; belowground active organic nitrogen, carbon, phosphorus; below ground activeinorganic nitrogen, carbon, phosphorus; atmospheric CO₂; below groundfertilizer nitrogen, carbon, phosphorus; carbon in cumulative CO₂evolved; cumulative nitrogen fixed; cumulative harvested plant nitrogenand phosphorus; total nitrogen, carbon, phosphorus additions; belowground labile nitrogen and phosphorus; net nitrogen, carbon, phosphoruschange; total nitrogen, carbon, phosphorus withdrawals; cumulative plantuptake of nitrogen and phosphorus; above ground rapid FOM nitrogen,carbon, phosphorus; below ground rapid FOM nitrogen, carbon, phosphorus;below ground resistant organic nitrogen, carbon, phosphorus; aboveground interim FOM carbon; below ground interim FOM carbon; above groundslow FOM nitrogen, carbon; below ground slow FOM nitrogen, carbon; belowground slow organic nitrogen, carbon; below ground slow inorganicnitrogen, carbon; below ground solution nitrogen, phosphate;recognizable standing dead nitrogen, carbon, phosphorus; total nitrogenthat can volatize; inorganic nitrogen in soil; cumulative nitrogenleached; organic nitrogen in soil; total nitrogen volatized; coldstress; drought; drought in stomatal conductivity; drought in turgidity;heat stress; nitrogen stress; phosphorus stress; photoperiod factor;cumulative drainage; potential cumulative evapotranspiration; potentialevapotranspiration daily; cumulative plant transpiration; planttranspiration daily; cumulative soil evaporation; soil evaporationdaily; cumulative evapotranspiration; evapotranspiration daily;cumulative irrigation; ponding height current; ponding height maximum;cumulative precipitation; cumulative runoff; potentially extractablewater; and water table depth.

Agronomic inputs can be broken down by soil layer (e.g., by depth), overdifferent time periods (for example, daily), and/or laterally (e.g., bylocation on a field). Lateral granularity can account for changes acrossa field or across multiple fields, such as changes in soil conditions,different crop/cultivar plantings in different locations on the samefield, or other changes. For example, for every soil layer and for everytime period agronomic outputs can also include, but are not limited to:new bulk density; downward water flux; net water flow; inorganicnitrogen in soil; root water uptake; dry weight of roots in the layer;soil temp; soil water content; soil hydraulic conductivity; upward waterflux; active, slow, resistant organic carbon content's rapid,intermediate, and slow; total fresh organic matter content; soil carboncontent; CO₂ sequestration; active, slow and resistant organic nitrogencontents; ammonia content; N₂O content; nitrogen content; urea content.

The agronomic simulator simulates agronomic activity based on providedagronomic inputs. The agronomic activity can be simulated using anagronomic model, such as the SYSTEM APPROACH TO LAND USE SUSTAINABILITY(SALUS) model or the CERES model. The SALUS model can model continuouscrop, soil, water, atmospheric, and nutrient conditions under differentmanagement strategies for multiple years. These strategies may havevarious crop rotations, planting dates, plant populations, irrigationand fertilizer applications, and tillage regimes. The model can simulateplant growth and soil conditions every day (during growing seasons andfallow periods) for any time period when weather sequences are availableor assumed. The model can account for farming and management practicessuch as tillage and residues, water balance, soil organic matter,nitrogen and phosphorous dynamics, heat balance, plant growth, plantdevelopment, presence of biotech traits, application of fungicides,application of pesticides, application of antimicrobials, application ofnucleic acids, and application of biologicals. The water balance canconsider surface runoff, infiltration, surface evaporation, saturatedand unsaturated soil water flow, drainage, root water uptake, soilevaporation and transpiration. The soil organic matter and nutrientmodel can simulate organic matter decomposition, nitrogen mineralizationand formation of ammonium and nitrate, nitrogen immobilization, gaseousnitrogen losses, and three pools of phosphorous.

The agronomic simulator can use any process or model that can predictagronomic outputs based on provided agronomic inputs. For instance, theagronomic simulator can use a physical, generative or mechanistic model;a purely statistical or machine learning model; or a hybrid. In anexample, the agronomic simulator can use a model that predicts agronomicoutputs by attempting to match (by exact match or approximate matchusing, for instance, nearest neighbor) the provided agronomic inputs ora transformation or function thereof (e.g., a dimensionality reduction,such as Principle Components Analysis or the outputs of an Indian BuffetProcess or other latent factor model) with a collection of previouslyobserved inputs and their matching outputs, and predicting the output ofthe matched input.

In some examples, an agronomic simulator can use one or morenon-analytic functions. An analytic function can be locally representedby a convergent power series; a non-analytic function cannot be locallyrepresented by a convergent power series.

Further description of the agronomic simulator is provided in U.S.patent application Ser. No. 15/259,030, titled “Agronomic Database andData Model” and filed on Sep. 7, 2016, the contents of which are herebyincorporated by reference herein to maximum extent permitted byapplicable law.

In some examples, some or all of the processing described above can becarried out on a personal computing device, on one or more centralizedcomputing devices, or via cloud-based processing by one or more servers.In some examples, some types of processing occur on one device and othertypes of processing occur on another device. In some examples, some orall of the data described above can be stored on a personal computingdevice, in data storage hosted on one or more centralized computingdevices, or via cloud-based storage. In some examples, some data arestored in one location and other data are stored in another location. Insome examples, quantum computing can be used. In some examples,functional programming languages can be used. In some examples,electrical memory, such as flash-based memory, can be used.

FIG. 5 is a block diagram of an example computer system 500 that may beused in implementing the technology described in this document.General-purpose computers, network appliances, mobile devices, or otherelectronic systems may also include at least portions of the system 500.The system 500 includes a processor 510, a memory 520, a storage device530, and an input/output device 540. Each of the components 510, 520,530, and 540 may be interconnected, for example, using a system bus 550.The processor 510 is capable of processing instructions for executionwithin the system 500. In some implementations, the processor 510 is asingle-threaded processor. In some implementations, the processor 510 isa multi-threaded processor. The processor 510 is capable of processinginstructions stored in the memory 520 or on the storage device 530.

The memory 520 stores information within the system 500. In someimplementations, the memory 520 is a non-transitory computer-readablemedium. In some implementations, the memory 520 is a volatile memoryunit. In some implementations, the memory 520 is a nonvolatile memoryunit.

The storage device 530 is capable of providing mass storage for thesystem 500. In some implementations, the storage device 530 is anon-transitory computer-readable medium. In various differentimplementations, the storage device 530 may include, for example, a harddisk device, an optical disk device, a solid-date drive, a flash drive,or some other large capacity storage device. For example, the storagedevice may store long-term data (e.g., database data, file system data,etc.). The input/output device 540 provides input/output operations forthe system 500. In some implementations, the input/output device 540 mayinclude one or more of a network interface devices, e.g., an Ethernetcard, a serial communication device, e.g., an RS-232 port, and/or awireless interface device, e.g., an 802.11 card, a 3G wireless modem, ora 4G wireless modem. In some implementations, the input/output devicemay include driver devices configured to receive input data and sendoutput data to other input/output devices, e.g., keyboard, printer anddisplay devices 560. In some examples, mobile computing devices, mobilecommunication devices, and other devices may be used.

In some implementations, at least a portion of the approaches describedabove may be realized by instructions that upon execution cause one ormore processing devices to carry out the processes and functionsdescribed above. Such instructions may include, for example, interpretedinstructions such as script instructions, or executable code, or otherinstructions stored in a non-transitory computer readable medium. Thestorage device 530 may be implemented in a distributed way over anetwork, for example as a server farm or a set of widely distributedservers, or may be implemented in a single computing device.

Although an example processing system has been described in FIG. 5,embodiments of the subject matter, functional operations and processesdescribed in this specification can be implemented in other types ofdigital electronic circuitry, in tangibly-embodied computer software orfirmware, in computer hardware, including the structures disclosed inthis specification and their structural equivalents, or in combinationsof one or more of them. Embodiments of the subject matter described inthis specification can be implemented as one or more computer programs,i.e., one or more modules of computer program instructions encoded on atangible nonvolatile program carrier for execution by, or to control theoperation of, data processing apparatus. Alternatively or in addition,the program instructions can be encoded on an artificially generatedpropagated signal, e.g., a machine-generated electrical, optical, orelectromagnetic signal that is generated to encode information fortransmission to suitable receiver apparatus for execution by a dataprocessing apparatus. The computer storage medium can be amachine-readable storage device, a machine-readable storage substrate, arandom or serial access memory device, or a combination of one or moreof them.

The term “system” may encompass all kinds of apparatus, devices, andmachines for processing data, including by way of example a programmableprocessor, a computer, or multiple processors or computers. A processingsystem may include special purpose logic circuitry, e.g., an FPGA (fieldprogrammable gate array) or an ASIC (application specific integratedcircuit). A processing system may include, in addition to hardware, codethat creates an execution environment for the computer program inquestion, e.g., code that constitutes processor firmware, a protocolstack, a database management system, an operating system, or acombination of one or more of them.

A computer program (which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code) can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astandalone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data (e.g., one ormore scripts stored in a markup language document), in a single filededicated to the program in question, or in multiple coordinated files(e.g., files that store one or more modules, sub programs, or portionsof code). A computer program can be deployed to be executed on onecomputer or on multiple computers that are located at one site ordistributed across multiple sites and interconnected by a communicationnetwork.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Computers suitable for the execution of a computer program can include,by way of example, general or special purpose microprocessors or both,or any other kind of central processing unit. Generally, a centralprocessing unit will receive instructions and data from a read-onlymemory or a random access memory or both. A computer generally includesa central processing unit for performing or executing instructions andone or more memory devices for storing instructions and data. Generally,a computer will also include, or be operatively coupled to receive datafrom or transfer data to, or both, one or more mass storage devices forstoring data, e.g., magnetic, magneto optical disks, or optical disks.However, a computer need not have such devices. Moreover, a computer canbe embedded in another device, e.g., a mobile telephone, a personaldigital assistant (PDA), a mobile audio or video player, a game console,a Global Positioning System (GPS) receiver, or a portable storage device(e.g., a universal serial bus (USB) flash drive), to name just a few.

Computer readable media suitable for storing computer programinstructions and data include all forms of nonvolatile memory, media andmemory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto optical disks; andCD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) or LCD (liquidcrystal display) monitor, for displaying information to the user and akeyboard and a pointing device, e.g., a mouse or a trackball, by whichthe user can provide input to the computer. Other kinds of devices canbe used to provide for interaction with a user as well; for example,feedback provided to the user can be any form of sensory feedback, e.g.,visual feedback, auditory feedback, or tactile feedback; and input fromthe user can be received in any form, including acoustic, speech, ortactile input. In addition, a computer can interact with a user bysending documents to and receiving documents from a device that is usedby the user; for example, by sending web pages to a web browser on auser's user device in response to requests received from the webbrowser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of what may beclaimed, but rather as descriptions of features that may be specific toparticular embodiments. Certain features that are described in thisspecification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable subcombination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various systemcomponents in the embodiments described above should not be understoodas requiring such separation in all embodiments, and it should beunderstood that the described program components and systems cangenerally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous. Other steps or stages may be provided,or steps or stages may be eliminated, from the described processes.Accordingly, other implementations are within the scope of the followingclaims.

Terminology

The phraseology and terminology used herein is for the purpose ofdescription and should not be regarded as limiting.

The term “approximately”, the phrase “approximately equal to”, and othersimilar phrases, as used in the specification and the claims (e.g., “Xhas a value of approximately Y” or “X is approximately equal to Y”),should be understood to mean that one value (X) is within apredetermined range of another value (Y). The predetermined range may beplus or minus 20%, 10%, 5%, 3%, 1%, 0.1%, or less than 0.1%, unlessotherwise indicated.

The indefinite articles “a” and “an,” as used in the specification andin the claims, unless clearly indicated to the contrary, should beunderstood to mean “at least one.” The phrase “and/or,” as used in thespecification and in the claims, should be understood to mean “either orboth” of the elements so conjoined, i.e., elements that areconjunctively present in some cases and disjunctively present in othercases. Multiple elements listed with “and/or” should be construed in thesame fashion, i.e., “one or more” of the elements so conjoined. Otherelements may optionally be present other than the elements specificallyidentified by the “and/or” clause, whether related or unrelated to thoseelements specifically identified. Thus, as a non-limiting example, areference to “A and/or B”, when used in conjunction with open-endedlanguage such as “comprising” can refer, in one embodiment, to A only(optionally including elements other than B); in another embodiment, toB only (optionally including elements other than A); in yet anotherembodiment, to both A and B (optionally including other elements); etc.

As used in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, i.e., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of or “exactly one of,” or, when used inthe claims, “consisting of” will refer to the inclusion of exactly oneelement of a number or list of elements. In general, the term “or” asused shall only be interpreted as indicating exclusive alternatives(i.e. “one or the other but not both”) when preceded by terms ofexclusivity, such as “either,” “one of,” “only one of,” or “exactly oneof.” “Consisting essentially of,” when used in the claims, shall haveits ordinary meaning as used in the field of patent law.

As used in the specification and in the claims, the phrase “at leastone,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

The use of “including,” “comprising,” “having,” “containing,”“involving,” and variations thereof, is meant to encompass the itemslisted thereafter and additional items.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed. Ordinal termsare used merely as labels to distinguish one claim element having acertain name from another element having a same name (but for use of theordinal term), to distinguish the claim elements.

What is claimed is:
 1. A method comprising: identifying, based on datafrom an agronomic simulation model, a first indication of existence of afirst agricultural characteristic in a particular portion of a firstgeographic region; receiving remote sensing data associated with thefirst geographic region, the received remote sensing data having beenobtained using one or more remote sensing devices; identifying, based onthe received remote sensing data, a second indication of existence of asecond agricultural characteristic in the particular portion of thefirst geographic region; determining that the second indication isdistinct from the first indication; and in response to determining thatthe second indication is distinct from the first indication, inferringone or more inputs to the agronomic simulation model based on thereceived remote sensing data to account for the existence of the secondagricultural characteristic as indicated by the second indication. 2.The method of claim 1, further comprising: identifying, based on seconddata from an agronomic simulation model, a third indication of existenceof a third agricultural characteristic in a particular portion of asecond geographic region; receiving second remote sensing dataassociated with the second geographic region, the received second remotesensing data having been obtained using one or more remote sensingdevices; identifying, based on the received second remote sensing data,a fourth indication of existence of a fourth agricultural characteristicin the particular portion of the second geographic region; determiningthat the fourth indication is substantially in accordance with the thirdindication; and based on determining that the fourth indication issubstantially in accordance with the third indication, confirming avalidity of the third indication of the existence of the thirdagricultural characteristic.
 3. The method of claim 1, wherein the firstagricultural characteristic is indicative of pollination,evapotranspiration and/or tasseling.
 4. The method of claim 1, whereinthe remote sensing data comprises infrared measurements, thermalmeasurements, visible light measurements, near-infrared measurements,measurements of ultraviolet light and other forms of electromagneticradiation, and/or aerially collected remote sensing data.
 5. The methodof claim 1, wherein identifying, based on data from the agronomicsimulation model, a first indication of the existence of a firstagricultural characteristic in a particular portion of a firstgeographic region includes: providing geographic data, other than thereceived remote sensing data, that identifies the particular portion ofthe first geographic region to an agronomic simulation model; andreceiving an output from the agronomic simulation model that includesdata identifying one or more agricultural characteristics that theagronomic simulation model predicts as existing within the particularportion of the first geographic region.
 6. The method of claim 5,wherein the one or more agricultural characteristics are predicted bythe agronomic simulation model based on an evaluation of rainfall, soilhydraulic conductivity, and elevation.
 7. The method of claim 1, whereinreceiving remote sensing data associated with the first geographicregion includes: receiving data indicative of one or more images of thefirst geographic region, the images having been captured by one or morecameras.
 8. The method of claim 7, wherein identifying, based on thereceived remote sensing data, a second indication of the existence ofthe second agricultural characteristic in the particular portion of thefirst geographic region includes: analyzing the one or more images ofthe first geographic region to determine whether the one or more imagesinclude an indication of the existence of the second agriculturalcharacteristic.
 9. The method of claim 8, wherein the secondagricultural characteristic includes ponding of water, tasseling and/orcanopy growth.
 10. The method of claim 1, wherein the one or more remotesensing devices include a camera coupled to a plane, drone, orsatellite.
 11. The method of claim 1, wherein inferring the one or moreinputs includes: adjusting one or more parameters of an agronomicsimulation model to account for the existence of the second agriculturalcharacteristic as indicated by the second indication, and/or adjusting aset of agronomic inputs to the agronomic simulation model to account forthe existence of the second agricultural characteristic as indicated bythe second indication.
 12. The method of claim 1, wherein determiningthat the second indication is distinct from the first indicationincludes determining that the second indication based on the remotesensing data identifies at least one agronomic characteristic that isnot modeled by the agronomic simulation model, and wherein the methodfurther comprises updating the agronomic simulation model to model theidentified at least one agronomic characteristic.
 13. The method ofclaim 1, wherein the first indication of the existence of a firstagricultural characteristic in a particular portion of a firstgeographic region includes data indicating the non-existence of thefirst agricultural characteristic, and wherein the second indication ofthe existence of a second agricultural characteristic in the particularportion of the first geographic region includes data indicating theexistence of the first agricultural characteristic.
 14. A method ofusing remote sensing data to infer one or more inputs to an agronomicsimulation model, the method comprising: receiving remote sensing dataassociated with a first geographic region, the received remote sensingdata having been obtained using one or more remote sensing devices;determining, based on the received remote sensing data, that one or moreportions of the first geographic region are associated with a particularagricultural characteristic; determining whether the particularagricultural characteristic is produced by one or more biotic factors;and in response to determining that the particular agriculturalcharacteristic is produced by the one or more biotic factors, inferringone or more inputs to the agronomic simulation model to account for theone or more biotic factors.
 15. The method of claim 14, furthercomprising: for the particular agricultural characteristic, calculating,by the agronomic simulation model, another agricultural characteristic,with the particular agricultural characteristic being attributable tothe other agricultural characteristic.
 16. The method of claim 15,wherein calculating the other agricultural characteristic includesback-calculating the other agricultural characteristic.
 17. The methodof claim 15, wherein the particular agricultural characteristiccomprises an emergence date and wherein the other agriculturalcharacteristic comprises a planting date.
 18. The method of claim 15,wherein the particular agricultural characteristic is indicative ofponding and wherein the other agricultural characteristic is indicativeof soil hydraulic conductivity.
 19. The method of claim 14, wherein theparticular agricultural characteristic is indicative of pollination,tasseling, evapotranspiration, a canopy, and/or a plant stand count. 20.The method of claim 14, wherein receiving remote sensing data associatedwith the first geographic region includes receiving data indicative ofone or more color images of the first geographic region, the colorimages having been captured by one or more cameras, and whereindetermining, based on the received remote sensing data, that one or moreportions of the first geographic region are associated with a particularagricultural characteristic includes analyzing the one or more colorimages of the first geographic region to determine whether the one ormore color images indicate an existence or value of a particularagricultural characteristic.
 21. The method of claim 20, wherein theparticular agricultural characteristic is an indication of agriculturalstress.
 22. The method of claim 20, wherein analyzing the one or morecolor images of the first geographic region to determine whether the oneor more color images indicate an existence or value of a particularagricultural characteristic includes: analyzing the one or more colorimages of the first geographic region to determine whether the one ormore color images include one or more indications of yellow vegetation.23. The method of claim 14, wherein determining whether the particularagricultural characteristic is produced by one or more biotic factorsincludes: providing geographic data, other than the received remotesensing data, that identifies the one or more portions of the firstgeographic region associated with the particular agriculturalcharacteristic to an agronomic simulation model; and receiving an outputfrom the agronomic simulation model that includes data indicatingwhether the particular agricultural characteristic associated with eachof the one or more respective portions of the first geographic region iscaused by one or more non-biotic factors.
 24. The method of claim 23,further comprising: based on a determination that the output from theagronomic simulation model indicates that the particular agriculturalcharacteristic associated with each of the one or more respectiveportions of the first geographic region is not caused by one or morenon-biotic factors, determining that the particular agriculturalcharacteristic is caused by one or more biotic factors.
 25. The methodof claim 23, further comprising: based on a determination that theoutput from the agronomic simulation model indicates that the particularagricultural characteristic associated with each of the one or morerespective portions of the first geographic region is caused by one ormore non-biotic factors, determining that the particular agriculturalcharacteristic is not caused by one or more biotic factors.
 26. Themethod of claim 14, wherein the remote sensing data comprises infraredremote sensing data, thermal measurements, and/or aerially collectedremote sensing data.
 27. The method of claim 14, wherein inferring oneor more inputs to the agronomic simulation model includes adjusting oneor more parameters of the agronomic simulation model to account for theone or more biotic factors.
 28. The method of claim 14, wherein thebiotic factors include existence of fungi, insects, and/or weeds, andthe non-biotic factors include soil pH, soil nitrogen levels, soilconsistency, soil depth, rainfall, phosphorus levels, and/or elevation.29. A system comprising: one or more computers and one or more storagedevices storing instructions that are operable, when executed by one ormore computers, to cause the one or more computers to perform operationscomprising: identifying, based on data from an agronomic simulationmodel, a first indication of existence of a first agriculturalcharacteristic in a particular portion of a first geographic region;receiving remote sensing data associated with the first geographicregion, the received remote sensing data having been obtained using oneor more remote sensing devices; identifying, based on the receivedremote sensing data, a second indication of existence of a secondagricultural characteristic in the particular portion of the firstgeographic region; determining that the second indication is distinctfrom the first indication; and in response to determining that thesecond indication is distinct from the first indication, inferring oneor more inputs to the agronomic simulation model based on the receivedremote sensing data to account for the existence of the secondagricultural characteristic as indicated by the second indication.
 30. Asystem comprising: one or more computers and one or more storage devicesstoring instructions that are operable, when executed by one or morecomputers, to cause the one or more computers to perform operationscomprising: receiving remote sensing data associated with a firstgeographic region, the received remote sensing data having been obtainedusing one or more remote sensing devices; determining, based on thereceived remote sensing data, that one or more portions of the firstgeographic region are associated with a particular agriculturalcharacteristic; determining whether the particular agriculturalcharacteristic is produced by one or more biotic factors; and inresponse to determining that the particular agricultural characteristicis produced by the one or more biotic factors, inferring one or moreinputs to the agronomic simulation model to account for the one or morebiotic factors.